Software Gigs

Design and architect next-generation ML inference infrastructure for globally distributed, multi-tenant model serving with high availability, scalability, and cost efficiency. Lead the development of low-latency, high-throughput inference systems supporting computer vision and multimodal models (CNNs, segmentation, object detection) using Scala, Java, and Go. Build large-scale distributed systems with reactive frameworks, integrate enterprise feature stores, and extend CI/CD pipelines (GitHub Prow, Pulumi) with automation and policy enforcement. Design advanced observability frameworks, optimize ML algorithms for performance, mentor engineers, and ensure MLOps and compliance standards (GDPR, SOC2) across the platform.

This role involves independently designing, developing, and debugging real-time embedded software, RTOS environments, and microcontroller firmware for wireless chipsets supporting 3G, 4G, 5G, and AI technologies. The engineer will work on driver development, integration, testing, and validation of communication protocols and platforms, while also contributing to data collection frameworks, machine learning modeling, and embedded software algorithm improvements. Responsibilities include on-target development, tool and process optimization, customer support, technical documentation, and active participation in design reviews. The position requires a Master's degree in Electrical Engineering, Computer Engineering, Computer Science, or related field.

Lead a specialized team of software engineers developing innovative Pixel sensor experiences at the intersection of hardware, software, and AI. You will drive the design and implementation of real-time embedded systems that process sensor signals efficiently while applying machine-learning and signal-processing algorithms in a power-optimized manner. Required: 8 years software development experience, 5 years in computer vision or signal processing, 5 years leading ML design and infrastructure optimization, and 3 years technical leadership. This role demands deep expertise in embedded software, AI/ML algorithms, sensor fusion, and the ability to independently architect systems while managing a team.

As a Senior Systems Engineer for Data Center AI at Qualcomm, you will research, develop, optimize, and validate AI/ML solutions that integrate Qualcomm's hardware accelerators with software and ecosystem to deliver high-performance inference in datacenter environments. You will design and deploy Gen AI and LLM applications, implement fine-tuning and distillation techniques, perform model benchmarking, and drive system-level architecture decisions. You will collaborate across functional teams to meet system-level requirements while staying current with advances in AI/ML models and hardware. Required skills include strong Python proficiency, expertise in ML frameworks (PyTorch, TensorFlow), deep understanding of ML deployment and system performance profiling, and experience with parallel computing.

Own and deliver small to medium machine learning system components from design through production deployment, including building data pipelines, training and evaluating models, and implementing MLOps monitoring. Write high-quality Python code to translate technical requirements into maintainable solutions, working with frameworks like scikit-learn, TensorFlow, PyTorch, and HuggingFace. Design and deploy ML models as microservices, APIs, batch jobs, or streaming components on AWS, with responsibility for model performance metrics, data drift detection, and retraining triggers. Collaborate across Data Engineers, Software Engineers, Data Scientists, and product stakeholders to deliver project objectives.

The AI Architect will lead the design, development, and deployment of enterprise-scale generative AI solutions across Azure and AWS, partnering with business and engineering leaders to define AI strategy and architecture standards. The role requires deep expertise in modern LLMs (Claude, Gemini, OpenAI), multi-cloud AI platforms, and enterprise architecture, with 10+ years in software/cloud architecture and 5+ years architecting AI/ML solutions. Key responsibilities include designing end-to-end GenAI solutions leveraging RAG, agentic AI, and multi-agent systems; establishing cloud-native deployment patterns; and defining governance, compliance, and responsible AI frameworks. The ideal candidate will serve as a trusted technical advisor to executives, mentor engineering teams, and drive AI adoption through reusable frameworks and reference architectures.

Architect responsible for creating detailed plans and drawings using AutoCAD and Revit to outline project design, conducting site evaluations to ensure quality and safety compliance, and overseeing the construction process from conception through completion. The role requires strong organizational and creative skills, knowledge of engineering and materials science, and ability to collaborate with multiple teams on goals, schedules, and budgets. Requires a Bachelor's degree in architecture and 3+ years of construction and architecture experience.

As a Staff Software Engineer in Data Platform Engineering, you will design, build, and operate cloud-native platform components and services, taking features from design through production. You'll lead well-scoped technical projects, collaborate with senior engineers on architecture alignment, and ensure reliability and scalability through testing and instrumentation. You'll spend most of your time hands-on building operators, controllers, automation, and platform services that other teams depend on, while mentoring junior engineers. The role requires 8+ years of software development experience (or equivalent with advanced degrees), 5+ years of hands-on Kubernetes experience, and proficiency with at least one major hyperscaler (AWS, Azure, GCP).

Own the complete release and deployment strategy for Elemynt's secure AI infrastructure platform, building systems that ensure every release is versioned, reproducible, observable, and safe to operate across enterprise and scientific computing environments. Design and implement deployment automation, CI/CD pipelines, artifact management, and operational visibility (logs, metrics, alerts) that work reliably in customer-managed and restricted environments. Debug incidents, identify root causes, and systematize fixes into reusable automation. Partner with product and engineering teams to embed deployment readiness into the platform from the start.

Build and operate the data and ML infrastructure powering an AI platform for materials science, owning both sides: data pipelines that ingest and curate large-scale scientific output into training-ready formats, and model packaging, serving, monitoring, and CI/CD systems that move models safely from research to production across customer environments. You will design data ingestion and transformation workflows, implement validation and quality gates, package and version models with reproducible builds, run models through batch and online inference with safe rollout and rollback, monitor for drift and degradation, and build observability and internal tooling for engineering and science teams. The role requires 6+ years shipping production software with deep expertise in data systems, ML infrastructure, containers, orchestration, and observability.

Design, build, and ship LLM-powered capabilities end to end—from prototyping and fine-tuning models to deploying production agents and retrieval systems. Own the full stack: prompt and context engineering, multi-step agent design with tool calling, RAG systems (embeddings, chunking, hybrid search, reranking), fine-tuning on multi-GPU with LoRA/QLoRA, evaluation and tracing infrastructure, and clean APIs for other engineers. Work in a secure, distributed environment where the platform runs on customer compute, cloud, or hybrid setups, requiring expertise with both commercial and self-hosted models.

As Principal Software Engineer for AI & Data Platform, you will architect the data foundation for scientific and engineering R&D platforms, designing scalable data processing patterns, ML training pipelines, and intelligent workflow interfaces. You will own end-to-end responsibilities including data modeling for multi-use analytics and ML, building production training and fine-tuning pipelines, model evaluation and benchmarking, and setting engineering standards for the team. The role requires 10+ years shipping production software, expert-level Python, deep experience with large-scale data systems (object storage, analytical processing, training formats), hands-on ML pipeline development, and the ability to set technical direction in early-stage environments while implementing it yourself.

Build the foundational agentic AI layer for a materials-science platform, including multi-model provider abstraction, agent orchestration with stateful checkpoints, retrieval systems, prompt versioning, and comprehensive tracing and evaluation frameworks. You'll design agents that plan and reason over tool calls in production, implement human-in-the-loop safety gates, and ensure all LLM behavior remains auditable and cost-tracked across customers' secure environments. The role demands deep production experience with agentic and LLM systems: async Python, structured outputs, memory and context management, multi-step workflow orchestration, and evaluation harnesses that catch regressions before deployment.

Staff Software Engineer leading the design, architecture, and operations of PlayStation's Real-Time Analytics Platform (RTAP), a large-scale distributed data system handling high-throughput, low-latency analytics and stream processing. The role involves architecting and evolving streaming and analytics platforms using Apache Flink, Spark, ClickHouse, and Druid; integrating batch and streaming workloads via lakehouse architectures; and embedding AI capabilities to automate engineering workflows and improve platform reliability. You will own critical platform capabilities end-to-end from design through production operations, mentor engineers, establish technical standards, and influence cross-functional technical strategy across PlayStation's data infrastructure.

Staff Software Engineer leading design and evolution of PlayStation's Real-Time Analytics Platform (RTAP), a large-scale distributed data system handling high-throughput, low-latency analytics and stream processing. Responsibilities include architecting stream processing pipelines, optimizing data services using Apache Flink, Spark, ClickHouse, and Druid, integrating batch and streaming workloads with lakehouse architectures, and building AI-powered capabilities for platform automation. The role requires deep expertise in distributed systems, real-time analytics, and mentoring engineering teams while establishing technical standards and best practices across the organization.

Ignite Digital seeks an AI Engineer to own the end-to-end machine learning lifecycle—from data preparation and model training through production deployment and monitoring. You will integrate AI models into frontend and backend systems, optimize performance for latency and cost, and collaborate with technical leads and customers to deliver Data and AI solutions. The role requires strong Python and cloud platform expertise (AWS, Azure, or GCP), software engineering fundamentals, and the ability to communicate technical opportunities and limitations across varying levels of technical experience.

Staff-level engineer designing and operating large-scale distributed data platforms powering real-time analytics across PlayStation. You will lead the architecture and evolution of the Real-Time Analytics Platform (RTAP), own critical capabilities end-to-end from design through production operations, and mentor engineers. Key responsibilities include building and optimizing distributed services using Apache Flink, Spark Structured Streaming, ClickHouse, and Apache Druid; integrating batch and streaming workloads with lakehouse architectures; and establishing technical standards for distributed systems, stream processing, and data modeling.

This Senior AI Software Program Manager role owns end-to-end planning, execution, and delivery of AI-based product programs—managing schedules, scope, budget, and resources while translating organizational strategy into product roadmaps. The individual coordinates across product management, technology teams, systems, and software leads to ensure timely, quality delivery; manages risk mitigation and stakeholder reporting; and drives process improvements and compliance across interdepartmental efforts. The role requires a Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, or related field, plus four years of related program or project management experience.

Staff GPU Software Engineer at Qualcomm's Adreno GPU Developer Tools team, designing and developing profiling, performance analysis, and debugging tools for next-generation graphics, games, ray tracing, machine learning, and image/video processing. Responsibilities include GPU hardware/software collaboration on performance validation, feature development, issue debugging, driver instrumentation, and developer ecosystem engagement. Requires 4+ years software engineering experience with strong C/C++ proficiency and GPU hardware/graphics knowledge; experience with graphics APIs (Vulkan, DirectX, Metal, OpenGL), game engines (Unity, Unreal Engine), or GPU computing (CUDA, OpenCL) is a plus.

Staff engineer role focused on integrating autonomy software across Shield AI's Hivemind platform, spanning simulation, hardware-in-the-loop, and flight systems. Responsibilities include developing deep expertise in the autonomy stack, configuring and deploying multi-agent systems, integrating new autonomy capabilities, enabling platform bring-up on new robotic hardware, architecting integration tooling and deployment infrastructure, ensuring cross-platform compatibility, diagnosing integration defects via telemetry analysis, and optimizing CI/CD workflows. The role bridges engineering development and real-world autonomous applications, requiring systems integration expertise, robotics platform knowledge, and hands-on proficiency with sensor drivers, middleware, and deployment frameworks.

Senior Software Engineer with 10+ years of experience to develop advanced algorithms and software applications using C++, Python, MATLAB, and TensorFlow in Linux environments. The role involves designing object-oriented software, prototyping AI/ML solutions, utilizing DevOps tools (GitLab, Docker, Podman), troubleshooting complex systems, and providing technical leadership to customers and internal teams. Strong mathematical foundation in probability, statistics, estimation theory, and machine learning is required to support algorithm development in a defense technology context.

Design and optimize video analysis and quality assessment algorithms for next-generation video codec hardware accelerators (FPGA/ASIC). Implement C-models and firmware for advanced video encoding, collaborating across algorithm, architecture, software, and hardware teams. Apply machine learning techniques including deep learning frameworks and transformer architectures for ROI detection, content understanding, and feature extraction. Integrate algorithms into production VOD/live streaming workflows and validate impact through A/B testing and objective quality metrics (VMAF, PSNR, SSIM).

Lead end-to-end product management for advisor-facing AI experiences within LPL Financial's AI Business Solutions team. Own product vision, roadmap, and delivery lifecycle from opportunity framing through commercialization and scaled adoption. Define advisor chat and agentic AI capabilities, evaluate third-party AI vendors, and ensure measurable business impact aligned to enterprise OKRs. Collaborate in a four-in-a-box model with Technology, Business, and Operations & Risk to co-design product definitions, data contracts, evaluation frameworks, and governance documentation.

The Applied AI Engineer develops, deploys, and maintains generative AI solutions for insurance products and internal workflows. The role involves implementing end-to-end AI pipelines using AWS services (SageMaker, Lambda, ECS/EKS, S3), building feature engineering workflows in Snowflake, and collaborating with cloud and MLOps teams. Candidates need 3+ years of AI/ML engineering experience with 1–2 years focused on generative AI or LLMs, proficiency in Python and frameworks like PyTorch or Hugging Face Transformers, and hands-on cloud deployment experience. The position requires monitoring model performance, ensuring security and compliance, and supporting AI architecture reviews.

Design and build large-scale multimodal foundation models that integrate high-dimensional biomedical imaging with molecular and language data. You will implement cross-domain data fusion strategies, develop high-performance ML pipelines for petabyte-scale datasets, and collaborate with scientists and engineers to translate biological complexity into scalable, production-ready code. The role requires deep expertise in Computer Vision architectures, distributed training frameworks, and expert Python proficiency. You will contribute through high-impact publications or open-source work and demonstrated ability to handle the noise and sparsity inherent in biological data.